Using location data and analytics from retail store traffic makes the process of retail site selection easier, faster and more trustworthy. Fueled by location intelligence from Unacast Now, we walk you through an example of using our data to assess different potential store locations in an urban trade area.
Why use location intelligence in Retail Site Selection?
Retail site selection isn't just retail real estate transactions. There's a whole world of retail store analytics packed-in to the site selection decision-making process. Key to attracting potential customers to any new location, is determining its potential for store foot traffic. In urban trade areas, where both foot traffic from potential consumers and retail competitor density tend to be high, using location intelligence to inform the site selection process is key.
As part of this process, many retailers first conduct a mapping of the relative foot traffic density in a given trade area. That will be our starting point for using Unacast Now to deftly and accurately inform the process of retail site selection.
Using Unacast Now for Retail Site Selection
Identifying the ideal new store location begins with assessing foot traffic density in the local catchment area, and measuring both the relative affluence of potential customers and thier propensity to cross-visit other venues and brands in the area, the combined gravity of which can draw foot traffic and potential customers away from your own prospective location.
To get started, we use the example of a limited service restaurant - a waffle retailer - parsing six potential locations fort a new store in the Washington, DC area. Off the top, we can identify potential locations on the map and simply click to zoom-in on any one location. Try clicking on the pin farthest to the left, below, to see what we mean.
The far left pin represents a potential retail store location in the Potomac Run Plaza. Off the top, we get some good summary content:
Potomac Run Plaza is in third place overall in our analysis. It has the most foot traffic, and visitation to similar restaurants in the area is also high. However, there is also a high amount of competing restaurants in this area.
Hmm. Not bad, but we need to know more. Let's drill down a little bit by clicking on some of the options to the left of that copy. You can toggle Traffic, Performance, Density, Area Distance, Area Median Income and Cross-visitation metrics on and off, so as to see the relative performance of each and the cumulative impact on the potential site's score. when we toggle Traffic and Area median Income to 'On', we see that the Potomac Run location jumps to the #2 potential location of the six measured.
So, if we believe a raw volume of foot traffic from potential customers with more expandable income is going to be key to our waffle store, this is not a bad option for our retail site selection. Let's go a little deeper into the location intelligence, though, and see what the data can tell us when comparing all six sites on equal terms
Grading potential locations to inform Retail Site Selection
Close the Potomac Run window by clicking on the X at the top right of the copy and you zoom out to a full map view again, this time with partial scores displayed for each of our six potential store locations. You'll see the score is visualized using a colorful little stacked bar at the bottom of the screen for each site in our trade area. Now toggle each one of the metrics to 'On'. See what happens?
A complete score is now displayed for each potential site, with both the total grade and the stacked bar updated. Keep in mind, the scores are relative to one another -- your best option based on the location intelligence is the one with a score of 100 or close to it. In this case, with all metrics toggled on and the underlying location analytics engine fully engaged, the top scoring potential site is at the Willston Center 2 venue. Here's a sample of the analysis:
People travel moderately far to visit Willston Centre II compared to other locations, and the median income of visitors is moderate compared to other locations. Visitors to nearby stores and businesses in this location are much more likely to visit a restaurant of this category compared to visitors in other candidate locations
Conducting this kind of spatial analysis from location intelligence, including customer foot traffic patterns, is essential to the business of retail real estate and extremely important to any potential site selection analyst or retail franchisee seeking the ideal market opportunity for a new store location.
Why Unacast?
Transparency - No more black box data solutions. Unacast provides the source IDs and source categories behind all of our geolocation data feeds to our clients, so you can gain further insight into the audience and feel confident about the authenticity of your location data.
Accuracy - We put all of our data through a strict cleansing process that includes deduplication, fraud detection and the removal of corrupt data and bid stream data. We gather geospatial data across multiple providers to ensure an accurate, full view of a user’s activity.
Unacast offers four types of location data feeds, customizable based on client needs:
Visits dataset - The Unacast Visits dataset is a database of confirmed visits to known points of interest (POIs). The Visits data feed includes a timestamp, average lat-long of the venue, venue-ID, venue name, brand name, venue ID, city, state, zip-code, dwell time, SIC category, NAICS category and stock ticker. This dataset is a good fit for clients who prefer to work with location data that has been contextualized and thoroughly vetted on accuracy.
Pure dataset - The Pure dataset is an unfiltered stream of location and geospatial data that Unacast has aggregated from our suppliers. The Pure dataset includes a timestamp, the lat-long coordinates of the interaction, horizontal accuracy, IP address and source-ID. This dataset is a good fit for investment firms prioritizing volume, with the internal capabilities to attach POI, analyze and extract value from alt data in a raw form.
Neighborhood dataset - The Neighborhood dataset describes foot traffic trends, traffic patterns and insights for Census Block Groups. These features are derived from billions of location signals per day sourced with GDPR, CCPA regulations, cleansed, processed and aggregated to Census Block Groups. The aggregates derived are extrapolated and supply corrected such that the features describe the US population.
Migration Patterns dataset - the Migration patterns dataset is a set of products for analyzing shifts in population. Currently, the package consists of two datasets:
Home-based Origin-Destination Flux (OD Flux) derives moves from changes in home location of a device and therefore is able to capture the origin and destination of each move. In order to assess that a move happened with certain confidence, an observation window of several weeks (currently 8 is required, which means that our insights are more certain, however, come with a delay of approximately half of the observation window (4 weeks).
Population Distribution Trends (PDT) is a metric that measures the proportion of devices in each state or county at a weekly snapshot. When a mass of people moves from one area to another, the proportion shifts accordingly. This metric is useful for analyzing the momentary situation, as it has no delay. Compared to OD Flux it doesn’t provide information about directionality of the moves or their permanency.
Want to learn more about retail traffic counts and location analysis, or inform the retail site selection process of your next business location. Join the many people and companies in the retail industry that are using Unacast Now to measure the performance of an existing location, predict the performance of a new location, and do a better job of reaching their target customer everywhere they are.
